2D Image reconstruction of nonwoven fabrics based on generative adversarial networks
Nonwovens are widely used as various filter materials,gas or particle adsorption materials,sound insulation materials,etc.in such fields as medical and health care,tourism,construction and waterproofing,and agriculture.The functions of nonwovens are closely related to the structure of their fiber collections.On the premise that nonwovens are randomly arranged in the layers of the fiber network,researchers usually use parametric simulation,fractal theory or image generation algorithms to simulate the fibers of nonwovens.However,the fiber structure generated in this way has the following problems:a)the fiber morphology based on parametric simulation is linear and does not agree with the actual fiber curl state;b)the fractal theory-based simulations cannot reconstruct the real nonwoven fabric structure concretely;c)the single fiber morphology reconstructed based on the generation algorithm is different from the real state of the fiber morphology.Toreconstruct a realistic image of the nonwoven fiber structure,this paper constructs a generative adversarial network(FGAN)with a multiscale training strategy on the GAN base framework.To improve the stability of model training,a multi-scale training strategy is used to train the model,and PixelwiseNormalization layer and standardization layer are also introduced in the construction of the model.The reconstructed fiber structure is randomly arranged,which is consistent with the real nonwoven fiber arrangement.To increase the diversity of fiber structures in the generated images,a weight diversity loss WMI Loss is proposed.the model is trained for a long time,and the final generator is stable to generate nonwoven images that are consistent with the real images.Compared with other generative models,FGAN has better stability and the reconstructed nonwoven images have higher quality and more diverse fiber structures.Also,to verify the effectiveness of multiple-degree training strategy and weight diversity loss,ablation experiments are performed on the model.The experimental results show that the evaluation index FID of the model-generated images is reduced by 24.52%under the effect of the multiscale training strategy and by 20.31%under the effect of the weight diversity loss.Finally,in order to verify the structural consistency between the generated images and the real images,the average porosity of the real images is calculated to be 30.87%and the average porosity of the generated images is 30.02%,which are very close to each other.And the pore number distribution curves of the generated image and the real image have a high overlap.From the above analysis,it can be verified that the porosity and pore distribution of the nonwoven fabric images generated by the model are consistent with the real images.FGAN can reconstruct high quality 2D images of nonwovens,from which detailed information on fiber distribution and morphology can be obtained.This information can be used for nonwoven performance analysis,optimization of production processes,etc.The method is able to reconstruct a diverse range of thin nonwoven structures,while the analysis of thicker nonwoven structures is yet to be investigated.
国家科技期刊平台
NSTL
万方数据
维普
